Access to Education & School Infrastructure: A Global Disparity

Introduction

Education is a right, not a privilege. Yet, millions of children around the world are denied access to basic schooling or learning infrastructure.

“Education is the most powerful weapon you can use to change the world.” — Nelson Mandela

Regional Analysis of Out-of-School Rates

Education accessibility differs drastically across regions. In this section, we explore the average out-of-school rate by global region in 2020, highlighting where the education gap is the widest and where progress has been made.

import pandas as pd
from plotnine import ggplot, aes, geom_bar, coord_flip, labs, theme_minimal,  scale_fill_brewer, theme

# dataset
newdata = pd.read_csv("/Users/pc/Desktop/education/unicef_indicator_regions122.csv")

# Filter for 2020
filtered = newdata[newdata['time_period'] == 2020]
region_avg = filtered.groupby('region', as_index=False)['number'].mean()
from plotnine import scale_fill_manual

purple_shades = ['#6C2856', '#6C2856', '#9C5878', '#B07C92', '#87326C']

(ggplot(region_avg, aes(x='reorder(region, number)', y='number', fill='region')) +
 geom_bar(stat='identity') +
 coord_flip() +
 labs(title='Average Out-of-School Rate by Region (2020)',
      x='Region', y='Out-of-School Rate (%)') +
 scale_fill_manual(values=purple_shades) +
 theme_minimal() + theme(figure_size=(10, 6)))

Top 10 Countries with the Highest Out-of-School Rates

Aound the world education

import pandas as pd
from plotnine import ggplot, aes, geom_bar, coord_flip, labs, theme_minimal

# data
df = pd.read_csv("/Users/pc/Desktop/education/unicef_indicator_2.csv")
df_2020 = df[(df["time_period"] == 2020) & (df["sex"] == "Total")]
df_2020_cleaned = df_2020[["country", "obs_value"]].dropna()
top10 = df_2020_cleaned.sort_values(by="obs_value", ascending=False).head(10)

# Plot
(ggplot(top10, aes(x='reorder(country, obs_value)', y='obs_value')) +
 geom_bar(stat='identity', fill='#6C2856') +
 coord_flip() +
 labs(title='Top 10 Countries with Highest Out-of-School Rates (2020)',
      x='Country', y='Out-of-School Rate (%)') +
 theme_minimal())

Top 5 Countries from African Region

Africa carries the heaviest burden when it comes to children missing out on school. Among all African nations, these five stand out with the highest out-of-school rates — painting a vivid picture of the challenges faced on the continent. South Sudan, sadly, tops the list highlighting the urgent need for stronger support. This highlights the higest content with the highest rate of out of school. Africa remains the most affected continent regarding school access.

import pandas as pd
from plotnine import ggplot, aes, geom_line, labs, theme_minimal, scale_fill_brewer

## datasets
edu = pd.read_csv("/Users/pc/Desktop/education/unicef_indicator_2.csv")
region_info = pd.read_csv("/Users/pc/Desktop/education/unicef_indicator_regions122.csv")

# Merge 
edu = edu.merge(region_info[['country', 'region']], on='country', how='left')
edu_2020 = edu[ (edu["sex"] == "Total")]
edu_2020 = edu_2020[['country', 'obs_value', 'region']].dropna()
edu_2020['obs_value'] = pd.to_numeric(edu_2020['obs_value'], errors='coerce')

# 10 countries
top10 = edu_2020.sort_values(by='obs_value', ascending=False).head(10)

#  Plot
(ggplot(top10, aes(x='reorder(country, obs_value)', y='obs_value', fill='country')) +
 geom_bar(stat='identity') +
 coord_flip() +
 labs(title='Top 5 African Countries with Highest Out-of-School Rates (2020)',
      x='Country', y='Out-of-School Rate (%)') +
 scale_fill_manual(values=['#6C2856', '#6C2856', '#9C5878', '#B07C92', '#87326C']) +
 theme_minimal())

Out-of-School Rate vs. GDP per Capita

Economic prosperity and education often go hand-in-hand./n Money matters — and so does education. In this plot, we explore how a country’s wealth (measured by GDP per capita) connects with school attendance rates. Do richer countries truly offer more children the chance to learn? Let’s find out. This scatterplot examines the relationship between a country’s wealth (GDP per capita) and the percentage of out-of-school children, revealing stark inequalities.

import pandas as pd
from plotnine import ggplot, aes, geom_point, labs, theme_minimal, geom_smooth

# datasets
edu = pd.read_csv("/Users/pc/Desktop/education/unicef_indicator_2.csv")
gdp = pd.read_csv("/Users/pc/Desktop/education/unicef_metadata.csv")

# Filter 
edu_total = edu[edu["sex"] == "Total"]
edu_total = edu_total[["country", "time_period", "obs_value"]].dropna()
edu_total.columns = ["country", "year", "out_of_school_rate"]

edu_latest = edu_total.sort_values('year', ascending=False).drop_duplicates('country')

gdp = gdp[["country", "GDP per capita (constant 2015 US$)", "year"]].dropna()
gdp_latest = gdp.sort_values('year', ascending=False).drop_duplicates('country')
gdp_latest.columns = ["country", "gdp_per_capita", "year"]
# Merge datasets
merged = pd.merge(edu_latest, gdp_latest, on="country").dropna()

# Plot
(ggplot(merged, aes(x='gdp_per_capita', y='out_of_school_rate')) +
 geom_point(size=2, color="#6C2856") +
 geom_smooth(method='lm', color="#B491A8", se=False) +  
 labs(title='Out-of-School Rate vs. GDP per Capita',
      x='GDP per Capita (USD)', y='Out-of-School Rate (%)') +
 theme_minimal())

import pandas as pd
import plotly.express as px

#datasets
edu = pd.read_csv("/Users/pc/Desktop/education/unicef_indicator_2.csv")
gdp = pd.read_csv("/Users/pc/Desktop/education/unicef_metadata.csv")
regions = pd.read_csv("/Users/pc/Desktop/education/unicef_indicator_regions122.csv")  
edu_total = edu[edu["sex"] == "Total"][["country", "time_period", "obs_value"]].dropna()
edu_total.columns = ["country", "year", "out_of_school_rate"]


edu_latest = edu_total.sort_values('year', ascending=False).drop_duplicates('country')

gdp = gdp[["country", "GDP per capita (constant 2015 US$)", "year"]].dropna()
gdp_latest = gdp.sort_values('year', ascending=False).drop_duplicates('country')
gdp_latest.columns = ["country", "gdp_per_capita", "year_gdp"]

# Merge 
merged = pd.merge(edu_latest, gdp_latest, on="country", how="inner")
merged = merged.merge(regions[['country', 'region']], on='country', how='left')

fig = px.scatter(
    merged,
    x='gdp_per_capita',
    y='out_of_school_rate',
    color='region',  
    hover_name='country', 
    hover_data={
        'gdp_per_capita': True,
        'out_of_school_rate': True,
        'region': True,
        'year': True
    },
    title='Out-of-School Rate vs. GDP per Capita (Latest Available Year)',
    labels={
        'gdp_per_capita': 'GDP per Capita (USD)',
        'out_of_school_rate': 'Out-of-School Rate (%)'
    },
    color_discrete_sequence=px.colors.qualitative.Set2
)

fig.update_traces(marker=dict(size=8))  
fig.update_layout(template="plotly_white") 
fig.show()

Out-of-School Rate Trends (2010–2021)

Over the last decade, how has the fight for education shifted? This chart follows the journey of different regions from 2010 to 2021, showing the ups and downs in school enrollment rates. Some regions have made real strides while others still have a long road ahead.

import pandas as pd
import plotly.express as px

# Load datasets
edu = pd.read_csv("/Users/pc/Desktop/education/unicef_indicator_2.csv")
regions = pd.read_csv("/Users/pc/Desktop/education/unicef_indicator_regions122.csv")  
edu_total = edu[edu["sex"] == "Total"][["country", "time_period", "obs_value"]].dropna()
edu_total.columns = ["country", "year", "out_of_school_rate"]

# Merge
edu_total = pd.merge(edu_total, regions[['country', 'region']], on='country', how='left')

edu_grouped = edu_total.groupby(['year', 'region']).out_of_school_rate.mean().reset_index()
edu_grouped['year'] = pd.to_numeric(edu_grouped['year'])

# Plot
fig = px.line(
    edu_grouped,
    x='year',
    y='out_of_school_rate',
    color='region',
    title='Out-of-School Rate Trends (2010–2021) by Region',
    labels={
        'year': 'Year',
        'out_of_school_rate': 'Average Out-of-School Rate (%)',
        'region': 'Region'
    },
    markers=True
)

fig.update_layout(template="plotly_white")
fig.show()

Global Map of Out-of-School Rates (2020)

Finally, a global choropleth map allows us to visually explore out-of-school rates across countries. Hovering over each country provides real-time insight into where efforts are most urgently needed.

import pandas as pd
import plotly.express as px

edu = pd.read_csv("/Users/pc/Desktop/education/new_data_withperc.csv")

# Filter 2020
newdata_2020 = newdata[newdata['time_period'] == 2020]
newdata_2020 = newdata_2020[['country', 'number']].dropna()
newdata_2020.columns = ['country', 'numberperc']

fig = px.choropleth(
    newdata_2020,
    locations="country",
    locationmode="country names",
    color="numberperc",
    color_continuous_scale="Purples",
    title="Out-of-School Rates Globally (2020)",
)
fig.show()